library(tidyverse) # for data cleaning and plotting
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.1.1 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(lubridate) # for date manipulation
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(openintro) # for the abbr2state() function
## Loading required package: airports
## Loading required package: cherryblossom
## Loading required package: usdata
library(palmerpenguins)# for Palmer penguin data
library(maps) # for map data
##
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
##
## map
library(ggmap) # for mapping points on maps
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.
library(gplots) # for col2hex() function
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
library(RColorBrewer) # for color palettes
library(sf) # for working with spatial data
## Linking to GEOS 3.9.1, GDAL 3.2.3, PROJ 7.2.1; sf_use_s2() is TRUE
library(leaflet) # for highly customizable mapping
library(carData) # for Minneapolis police stops data
library(ggthemes) # for more themes (including theme_map())
theme_set(theme_minimal())
# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")
## Rows: 25600 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): Brand, Store Number, Store Name, Ownership Type, Street Address, C...
## dbl (2): Longitude, Latitude
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
starbucks_us_by_state <- Starbucks %>%
filter(Country == "US") %>%
count(`State/Province`) %>%
mutate(state_name = str_to_lower(abbr2state(`State/Province`)))
# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
place = c("Home", "Macalester College", "Adams Spanish Immersion",
"Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
"Dance Spectrum", "Pizza Luce", "Brunson's"),
long = c(-93.1405743, -93.1712321, -93.1451796,
-93.1650563, -93.1542883, -93.1696608,
-93.1393172, -93.1524256, -93.0753863),
lat = c(44.950576, 44.9378965, 44.9237914,
44.9654609, 44.9295072, 44.9436813,
44.9399922, 44.9468848, 44.9700727)
)
#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")
## Rows: 40718 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): state, fips
## dbl (2): cases, deaths
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
If you were not able to get set up on GitHub last week, go here and get set up first. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):
keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).Put your name at the top of the document.
For ALL graphs, you should include appropriate labels.
Feel free to change the default theme, which I currently have set to theme_minimal().
Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!
When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.
These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.
ggmap)Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?world <- get_stamenmap(
bbox = c(left = -180, bottom = -57, right = 179, top = 82.1),
maptype = "terrain",
zoom = 2)
## Source : http://tile.stamen.com/terrain/2/0/0.png
## Source : http://tile.stamen.com/terrain/2/1/0.png
## Source : http://tile.stamen.com/terrain/2/2/0.png
## Source : http://tile.stamen.com/terrain/2/3/0.png
## Source : http://tile.stamen.com/terrain/2/0/1.png
## Source : http://tile.stamen.com/terrain/2/1/1.png
## Source : http://tile.stamen.com/terrain/2/2/1.png
## Source : http://tile.stamen.com/terrain/2/3/1.png
## Source : http://tile.stamen.com/terrain/2/0/2.png
## Source : http://tile.stamen.com/terrain/2/1/2.png
## Source : http://tile.stamen.com/terrain/2/2/2.png
## Source : http://tile.stamen.com/terrain/2/3/2.png
ggmap(world) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude, color= `Ownership Type`),
alpha = 0.3,
size = 0.1) +
scale_fill_viridis_c(option = "inferno") +
theme_map() +
labs(title="World map locations of Starbucks by ownership type")+
theme(legend.background = element_blank())
## Warning: Removed 1 rows containing missing values (geom_point).
I can deduce that in the American continent most Starbucks are licensed or company owned.Such is the case for almost every other region except for East Asia, where most businesses are joint ventures. Also, Most of the Starbucks seems to concentrate in North America and East Asia.
saint_paul <- get_stamenmap(
bbox = c(left = -93.4775, bottom = 44.8240, right = -92.7992, top = 45.1615),
maptype = "terrain",
zoom = 11)
## Source : http://tile.stamen.com/terrain/11/492/735.png
## Source : http://tile.stamen.com/terrain/11/493/735.png
## Source : http://tile.stamen.com/terrain/11/494/735.png
## Source : http://tile.stamen.com/terrain/11/495/735.png
## Source : http://tile.stamen.com/terrain/11/496/735.png
## Source : http://tile.stamen.com/terrain/11/492/736.png
## Source : http://tile.stamen.com/terrain/11/493/736.png
## Source : http://tile.stamen.com/terrain/11/494/736.png
## Source : http://tile.stamen.com/terrain/11/495/736.png
## Source : http://tile.stamen.com/terrain/11/496/736.png
## Source : http://tile.stamen.com/terrain/11/492/737.png
## Source : http://tile.stamen.com/terrain/11/493/737.png
## Source : http://tile.stamen.com/terrain/11/494/737.png
## Source : http://tile.stamen.com/terrain/11/495/737.png
## Source : http://tile.stamen.com/terrain/11/496/737.png
## Source : http://tile.stamen.com/terrain/11/492/738.png
## Source : http://tile.stamen.com/terrain/11/493/738.png
## Source : http://tile.stamen.com/terrain/11/494/738.png
## Source : http://tile.stamen.com/terrain/11/495/738.png
## Source : http://tile.stamen.com/terrain/11/496/738.png
ggmap(saint_paul) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude),
alpha = 0.3,
size = 1) +
scale_fill_viridis_c(option = "inferno") +
theme_map() +
labs(title="Saint Paul locations of Starbucks")+
theme(legend.background = element_blank())
## Warning: Removed 25493 rows containing missing values (geom_point).
It changes the details in the graphs such as clear terrain delimitation and clear region labels.
get_stamenmap() in help and look at maptype). Include a map with one of the other map types.saint_paul2 <- get_stamenmap(
bbox = c(left = -93.4775, bottom = 44.8240, right = -92.7992, top = 45.1615),
maptype = "toner-background",
zoom = 11)
## Source : http://tile.stamen.com/toner-background/11/492/735.png
## Source : http://tile.stamen.com/toner-background/11/493/735.png
## Source : http://tile.stamen.com/toner-background/11/494/735.png
## Source : http://tile.stamen.com/toner-background/11/495/735.png
## Source : http://tile.stamen.com/toner-background/11/496/735.png
## Source : http://tile.stamen.com/toner-background/11/492/736.png
## Source : http://tile.stamen.com/toner-background/11/493/736.png
## Source : http://tile.stamen.com/toner-background/11/494/736.png
## Source : http://tile.stamen.com/toner-background/11/495/736.png
## Source : http://tile.stamen.com/toner-background/11/496/736.png
## Source : http://tile.stamen.com/toner-background/11/492/737.png
## Source : http://tile.stamen.com/toner-background/11/493/737.png
## Source : http://tile.stamen.com/toner-background/11/494/737.png
## Source : http://tile.stamen.com/toner-background/11/495/737.png
## Source : http://tile.stamen.com/toner-background/11/496/737.png
## Source : http://tile.stamen.com/toner-background/11/492/738.png
## Source : http://tile.stamen.com/toner-background/11/493/738.png
## Source : http://tile.stamen.com/toner-background/11/494/738.png
## Source : http://tile.stamen.com/toner-background/11/495/738.png
## Source : http://tile.stamen.com/toner-background/11/496/738.png
ggmap(saint_paul2) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude),
alpha = 0.3,
size = 1) +
scale_fill_viridis_c(option = "inferno") +
theme_map() +
labs(title="Saint Paul locations of Starbucks")+
theme(legend.background = element_blank())
## Warning: Removed 25493 rows containing missing values (geom_point).
annotate() function (see ggplot2 cheatsheet).ggmap(saint_paul) +
geom_point(data = Starbucks,
aes(x = Longitude, y = Latitude),
alpha = 0.3,
size = 1) +
annotate('rect',xmin= -93.1766, ymin= 44.9402, xmax= -93.1620, ymax= 44.9354, col="red", fill="red")+
annotate('text', x=-93.1766, y=45.0, label = 'Macalester College', colour = I('red'), size = 5)+
annotate('segment', x=-93.1766, xend= -93.175, y=44.990, yend=44.950,
colour=I('red'), arrow = arrow(length=unit(0.3,"cm")), size = 1.5) +
scale_fill_viridis_c(option = "inferno") +
theme_map() +
labs(title="Macalester College")+
theme(legend.background = element_blank())
## Warning: Removed 25493 rows containing missing values (geom_point).
geom_map())The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.
census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>%
separate(state, into = c("dot","state"), extra = "merge") %>%
select(-dot) %>%
mutate(state = str_to_lower(state))
## Rows: 51 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (1): est_pop_2018
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
starbucks_with_2018_pop_est <-
starbucks_us_by_state %>%
left_join(census_pop_est_2018,
by = c("state_name" = "state")) %>%
mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)
dplyr review: Look through the code above and describe what each line of code does.First line reads in the data set saved in dropbox. Second line separates a variable that initially included valued of the name state with a dot into two where the dot is apart from the name Third line omits the column that only includes the dot Fourth line modifies the column state for the values to be all lower case.
First and second line of second part creates a new data set using the starbucks_us_by_state data set Third and fourth Line joins the starbucks_us_by_state to the census_pop_est_2018 by the name of the state to get a data set that not only has the starbucks per state but also the population per state. Fifth line creates a variable starbucks_per_10000 by dividing the number of starbucks per state by their population and multiplying by 10000
states_map <- map_data("state")
starbucks_with_2018_pop_est %>%
ggplot() +
geom_map(map = states_map,
aes(map_id = state_name,
fill = starbucks_per_10000)) +
geom_point(data= Starbucks %>%
filter("state_name"!="hawaii|alaska",
Country == "US"),
aes(x=Longitude,y=Latitude),
size = 0.05,
alpha= 0.02,
color= "goldenrod")+
expand_limits(x = states_map$long, y = states_map$lat) +
theme_map()+
theme(legend.background = element_blank())+
labs(title="Starbucks per 10000 people")
It is easy to see that there are places, especially bigger cities, with more starbucks and more people, that have a higher count of starbucks businesses.
leaflet)tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.favorite_stp_by_Marcela <- tibble(
place = c("Buffalo Wild Wings", "Macalester College", "Master Noodle",
"Stadium", "Whole Foods", "Wet Paint",
"Pad Thai", "GrandView Theater", "CVS", "Spanish House"),
long = c(-93.165937, -93.1712321, -93.156745,
-93.1650563, -93.166927, -93.172050,
-93.171422, -93.177650, -93.177076, -93.170317),
lat = c(44.943604, 44.9378965, 44.955839,
44.952555, 44.946689, 44.940048,
44.940158, 44.940055, 44.940446, 44.935609)
)
leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.leaflet(favorite_stp_by_Marcela) %>%
addTiles() %>%
addCircles()
## Assuming "long" and "lat" are longitude and latitude, respectively
leaflet(favorite_stp_by_Marcela) %>%
addTiles() %>%
addCircles() %>%
addPolylines(lng= ~long,
lat= ~lat,
color=col2hex("goldenrod"))
## Assuming "long" and "lat" are longitude and latitude, respectively
Wanted to add the labels :)
leaflet(favorite_stp_by_Marcela) %>%
addTiles() %>%
addCircles(label= ~place) %>%
addPolylines(lng= ~long,
lat= ~lat,
color=col2hex("goldenrod"))
## Assuming "long" and "lat" are longitude and latitude, respectively
This section will revisit some datasets we have used previously and bring in a mapping component.
The data come from Washington, DC and cover the last quarter of 2014.
Two data tables are available:
Trips contains records of individual rentalsStations gives the locations of the bike rental stationsHere is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.
data_site <-
"https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds"
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")
## Rows: 347 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): name
## dbl (4): lat, long, nbBikes, nbEmptyDocks
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.departures_per_station <- Trips %>%
group_by(sstation) %>%
summarize(departures_per_sstation = n())
station_joined <- Stations %>%
left_join(departures_per_station,
by = c("name" = "sstation"))
washington <- get_stamenmap(
bbox = c(left = -77.2622, bottom= 38.8208, right= -76.7660, top= 38.9897),
maptype = "terrain",
zoom = 11
)
## Source : http://tile.stamen.com/terrain/11/584/782.png
## Source : http://tile.stamen.com/terrain/11/585/782.png
## Source : http://tile.stamen.com/terrain/11/586/782.png
## Source : http://tile.stamen.com/terrain/11/587/782.png
## Source : http://tile.stamen.com/terrain/11/584/783.png
## Source : http://tile.stamen.com/terrain/11/585/783.png
## Source : http://tile.stamen.com/terrain/11/586/783.png
## Source : http://tile.stamen.com/terrain/11/587/783.png
## Source : http://tile.stamen.com/terrain/11/584/784.png
## Source : http://tile.stamen.com/terrain/11/585/784.png
## Source : http://tile.stamen.com/terrain/11/586/784.png
## Source : http://tile.stamen.com/terrain/11/587/784.png
ggmap(washington) +
geom_point(data= station_joined,
aes(x= long, y= lat, color= departures_per_sstation))+
theme_map()+
theme(legend.background = element_blank())
## Warning: Removed 45 rows containing missing values (geom_point).
casual_rider <- Trips %>%
group_by(sstation) %>%
summarise(casual= sum(client == "Casual"),ntotal=n()) %>%
mutate(portionCasual= casual/ntotal) %>%
left_join(Stations, by=c("sstation"="name"))
ggmap(washington)+
geom_point(data= casual_rider,
aes(x=long, y=lat, color= portionCasual))+
scale_color_viridis_c()
## Warning: Removed 55 rows containing missing values (geom_point).
The following exercises will use the COVID-19 data from the NYT.
states_map <- map_data("state")
covid19 %>%
mutate(state = str_to_lower(state)) %>%
ggplot()+
geom_map(map= states_map,
aes(map_id = state,
fill= fips))+
expand_limits(x= states_map$long, y= states_map$lat)+
theme_map()
It is not really intuitive what is going on in this graph given that the legend colors has so many arguments of similar tone. Also, given that we are not accounting for population, it might give a misleading perception of cases per state.
covid_with_2018_pop_est <-
covid19 %>%
mutate(state = str_to_lower(state)) %>%
left_join(census_pop_est_2018,
by = c("state" = "state")) %>%
mutate(covid_per_10000 = (as.numeric(fips)/est_pop_2018)*10000)
covid_with_2018_pop_est %>%
ggplot()+
geom_map(map= states_map,
aes(map_id = state,
fill= covid_per_10000))+
expand_limits(x= states_map$long, y= states_map$lat)+
theme_map()
Now we are able to differentiate the severity of the cumulative cases per state relative to their total population.
These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.
MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.mpls_suspicious <- MplsStops %>%
group_by(neighborhood) %>%
summarize(cases_per_neighborhood = n(),
suspicious_prop = sum(problem == "suspicious")/cases_per_neighborhood)
head(mpls_suspicious)
leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.suspicious <- colorFactor("viridis",
domain = MplsStops$problem)
leaflet(data= MplsStops) %>%
addTiles() %>%
addCircleMarkers(lng= ~long,
lat= ~lat,
label= ~problem,
color= ~suspicious(problem),
stroke = FALSE)
eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.mpls_nbhd <- st_read("/Users/marcelasaavedragonzalez/Desktop/Data Science/marcela_test_repo/Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)
head(MplsDemo)
mpls_all <- mpls_nbhd %>%
left_join(mpls_suspicious, by=c("BDNAME"="neighborhood")) %>%
left_join(MplsDemo, by=c("BDNAME"="neighborhood"))
leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.prop_sus <- colorNumeric("inferno",
domain= mpls_all$suspicious_prop)
leaflet(mpls_all) %>%
addTiles() %>%
addPolygons(
fillColor = ~prop_sus(suspicious_prop),
fillOpacity = 0.8
)
leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.Which Neighborhoods have highest incidence of stops by 1000 people?
cases_per_1000 <- mpls_all %>%
mutate("cases_per_1000"= (cases_per_neighborhood/population)*1000)
stop_per_1000 = colorNumeric("viridis",
domain = cases_per_1000$cases_per_1000)
leaflet(cases_per_1000) %>%
addTiles() %>%
addPolygons(
fillColor = ~stop_per_1000(cases_per_1000),
fillOpacity = 0.8,
label= ~cases_per_1000
)
link here: !()[https://github.com/MarcelaSaavedraG/marcela_test_repo/tree/main/Exercises/Exercise%204]
DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?